Happiness has been a central tenet of Western culture since the days of Greek philosophers (Graham 2008). Although there are many definitions of happiness, generally, it could be defined as an abstract and subjective assessment of oneself or the holistic assessment of one’s entire life (Veenhoven 2008). Happiness is often described as the main aim of life and an individual’s drive for personal fulfillment (Scoffham and Barnes 2011, Weech-Maldonado R. et al. 2017).
Nowadays, as mental health problem becomes one common societal issue, people’s well-being has attracted policymakers’ attention worldwidely. Happiness is not a purely personal issue; it is strongly determined by the society they live in (Frey & Stutzer 2002: p. vii). On one hand, some reported satisfaction with life across countries is positively correlated with average income, in other words, people in richer countries are likely to be happier than those in poor countries in a long term (Easterlin et al. 2010). On the other hand, there are also findings showing the income inequality, perceived unfairness and lack of trust rather than the income level lead more negative effect on people’s well-being (Oishi et al. 2011). Besides, Cheng et al. (2014) find more factors, including universal disposition, cultural self-construal, and national income can elucidate differences in subjective well-being through a multilevel structural equation model. Moreover, Cordero et al. (2017) claim that age, marital status, religion and unemployment, these kinds of traditional determinants also have a significant impact on efficiency of converting resources into higher level of happiness. The arguments about the factors affecting people’s well-being and the ability to transfer resources into higher level happiness are complex.
The most professional report about states’ happiness index is the World Happiness Report, which is distributed annually by the United Nations Sustainable Development Solutions Network based on respondent ratings of their own lives, the happiness index correlates with various life factors, such as financial generation, social back, life anticipation, flexibility, nonattendance of debasement, and liberality. The original dataset for the report provides a populated-weighted average score on a scale running from 0 to 10 for each of the variables from these six aspects, including 1) real GDP per capita, 2) social support, 3) healthy life expectancy, 4) freedom to make life choices, 5) generosity, 6) perceptions of corruption.
Global happiness report is a landmark survey of the state of global happiness and has attracted more and more attention of policymakers from many areas, including the governments, organizations, and civil society, etc. But the factors listed in the report are not complete. This research aims to explore the factors that are not listed in the World Happiness Report and have imposed effect on the improvement of happiness index in 2020.
In this research, I would like to explore the following questions:
Firstly, does the regime type, measured in the scale of two dimensions – democracy and autocracy – have an influence on happiness index?
Secondly, do the demographic factors, such as population density, population net change, and net migrants impose an impact on national happiness score?
Thirdly, since the first identification coronavirus case in December 2019, the virus has taken the world by storm. Besides the economic impact, the pandemic period imposes great impact on people’s mental health impacts, which could not be overlooked.
Thus, what are the effects of the mortality due to Covid-19 and the index of exposure to COVID-19 infections in other countries on people’s happiness index?
Regime type contains two dimensions – autocracy and democracy. Different scales of autocracy and democracy will reflect the political environment inside the country. It could indicate people’s freedom to make life choices to some degree, but also indicate citizen’s political freedom. In complete autocracy, there is no multiparty elections for the chief executive or the legislature in one country (e.g., North Korea since 1945). In some electoral autocracy (e.g., Iran since 1980, Pakistan since 2002, Turkey since 2014, etc.), even there are multiparty elections, but the elections are unfair and not free, or no multiparty elections. In liberal democracy, which has free and fair multiparty elections, and also has access to justice, transparent law enforcement, and the liberal principles of respect for personal liberties, the rule of law, and judicial as well as legislative constraints on the executive (e.g., Australia since 1901, most of the European countries, United States, Canada, and Japan in the modern periods) (Coppedge et al. 2020, p. 266). But we could not ignore there are also some incomplete democracies which has free and fair multiparty elections in reality, but either access to justice, or transparent law enforcement, or liberal principles of respect for personal liberties, rule of law, and judicial as well as legislative constraints on the executive are not satisfied (e.g., India since 1952 (except for 1975 and 1976), Peru during 1981-1991 and 2001-till date, Argentina since 1984, Mexico since 1995, etc.). Generally, when the democracy level is higher, people will feel more satisfied since they feel more engaged in the political decision-making process.
Research focusing on socio-demographic factors find that socio-demographic differences, such as gender, age, race, income, and education, etc., can impact an individual’s level of happiness (Kim-Prieto et al. 2005). When comparing across countries, the population size, population density, the population net change, the amount of net immigrant might impose an effect on people’s satisfaction about the living environment. For instance, in the countries with similar natural resources, the one with high population density will have fewer natural resources per capita compared to the other countries with lower population density. Similarly, when there are rapidly increase in the population size and net immigrant, citizens generally feel more stressed since there will be higher competition in the employment, social resources, etc.
COVID-19, which was first discovered and reported in Wuhan, China in December 2019, spread across the world at a fast and terrifying pace throughout 2020. The pandemic has affected many key aspects of life around the world. The most severe impact of the pandemic is the 2 million deaths from COVID-19 in 2020. The nearly 4 per cent annual increase in deaths worldwide represents a serious loss of social welfare. In terms of life, financial insecurity, anxiety, disruption in every aspect of life, stress and challenges to mental and physical health for many people (World Happiness Report, 2021, p. 7). In fact, countries with the highest deaths also had the greatest falls in GDP per head (p. 8). Thus, COVID-19 deaths in 2020, excess deaths in 2020 relative to 2017-2019 average, and index of exposure to COVID-19 infections in other countries might have negative effect on people’s happiness index.
Here are the three main hypotheses in this research:
H1: More democratic a country’s regime type is, the higher happiness scores the country will have.
H2: Population size, population density, population net change, net migrants, and population density net change are negatively correlated with happiness index.
H3: The serious the pandemic in one country, measured by COVID-19 deaths per 100,000 population in 2020, Excess deaths in 2020 per 100,000 population, relative to 2017-2019 average, and Index of exposure to COVID-19 infections in other countries, the more decrease in the country’s happiness index at the same year.
The new dataset is generated based on the following five datasets:
Each dataset is based on the survey collected from people in over 150 countries. The 2020 dataset features the happiness score averaged over the years 2017–2019, and the 2021 one features the happiness score averaged over the years 2018–2020. I have merged the two datasets and has built the difference in happiness score, difference in logged GDP per capita, difference in social support, difference in healthy life expectancy, difference in freedom to make life choices, difference in generosity, and difference in perceptions of corruption seven variables. The main variable happiness Score and the new generated difference in happiness score will be used as the dependent variable in my research. The Happiness Score is a national average of the responses to the main life evaluation question asked in the Gallup World Poll (GWP), which uses the Cantril Ladder. There are also six variables correlated to happiness score, each of which is measured reveals a populated-weighted average score on a scale running from 0 to 10, including 1) real GDP per capita, 2) social support, 3) healthy life expectancy, 4) freedom to make life choices, 5) generosity, 6) perceptions of corruption. These six metrics are used to explain extent to which each factors contribute to increasing life satisfaction when compared to the hypothetical nation of Dystopis, which represents the lowest national averages for each key variable.
This dataset is created by Tanu N Prabhu using web scraping based on the data shown on the website named worldometer, which is a real-time monitoring and reporting website about the world and national population from many demographic aspects. The dataset is posed on Kaggle website, containing the variables Population (2020), Density (P/Km2), Land Area (Km²), Population Net Change, and Migrants (net) I want to use to explore the impact of demographic factors on Happiness Score.
This dataset is downloaded from the World Happiness Report 2021 website and has been included as the appendices data to explore the effect of COVID-19 on people’s well-being. There are several variables to measure the COVID-19. I utilize the COVID-19 deaths per 100,000 population in 2020, Excess deaths in 2020 per 100,000 population, relative to 2017-2019 average, and Index of exposure to COVID-19 infections in other countries to explore how COVID-19 affect people’s well-being in 2020.
The Polity5 dataset covers all major, independent states in the global system over the period 1800-2018. I will include a Regime variable from the dataset’s Polity2 index (Marshall and Jaggers 2015). The Polity conceptual scheme is unique in that it examines concomitant qualities of democratic and autocratic authority in governing institutions, rather than discreet and mutually exclusive forms of governance. This perspective envisions a spectrum of governing authority that spans from fully institutionalized autocracies through mixed, or incoherent, authority regimes (termed “anocracies”) to fully institutionalized democracies. The “Polity Score” captures this regime authority spectrum on a 21-pont scale ranging from -10 (hereditary monarchy) to +10 (consolidated democracy). The Polity scores can also be converted into regime categories in a suggested three-part categorization of “autocracies” (-10 to -6), “anocracies” (-5 to +5 and three special values: -66, -77 and -88), and “democracies” (+6 to +10).
In this research, I will firstly build an original dataset based on the five datasets: World Happiness Report (2020), World Happiness Report (2021), Population by Country up to 2020, Covid-19 Mortality Dataset, and Polity IV Dataset. I will use Pandas in python to clean, merge, sort the datasets, and build several new variables to measure the difference in happiness score, GDP per capita, social support, healthy life expectancy, freedom to make life choices, generosity, perceptions of corruption, and population density between 2019 and 2020.
I build five OLS linear regression models to test the factors affecting people’s happiness index in 2020 and the factors affecting the improvement of happiness score in 2020. Based on the results, I will further interpret it and use data visualization to explore further farther.
To visualize the correlation between the factors and the happiness score in 2020, the factors and the differences in happiness score in 2020, I use R, python, shiny, and D3 to plot the frequency table, variable width column charts, small multiple bar charts, line charts, scatter charts, etc.
Table 1 shows the statistical results of five linear regression models. In model 1-3, I use Happiness Score in 2021 report as the dependent variable. Model 1 is the basic model which contains the six factors included in the panel data for World Happiness Report 2021, including 1) GDP per capita, 2) social support, 3) healthy life expectancy, 4) freedom to make life choices, 5) generosity, 6) perceptions of corruption. Model 2 contains regime type, five demographic variables, including population size (logged), population density (logged P/Km²), population change (net), migrants (net), population density net change besides the six basic variables included in Model 1. Model 3 includes three variables measuring the severity of COVID-19, specifically COVID-19 deaths per 100,000 population in 2020, Excess deaths in 2020 per 100,000 population, relative to 2017-2019 average, and Index of exposure to COVID-19 infections in other countries besides including the variables those are in Model 2.
In model 4-5, I use Change in Happiness Score between average happiness score during 2018-2020 and 2017-2019 as dependent variable. As for the explanatory variables, I have included change in GDP per capita (logged), change in social support, change in healthy life expectancy, change in freedom to make life choices, change in generosity, change in perceptions of corruption, and change in population density between 2019 and 2020 in Model 4. I have added one demographic variables – change in population density and three COVID-19 severity measurement variables – COVID-19 deaths per 100,000 population in 2020, Excess deaths in 2020 per 100,000 population, relative to 2017-2019 average, and Index of exposure to COVID-19 infections in other countries in Model 5 besides the same variables as those included in Model 4.
From the statistical regression results, Model 1 provides support to the correlations between the five factors included in the panel data for World Happiness Report 2021 with happiness score. Specifically, there are positive correlations between the first four variables – GDP per capita (logged), social support, healthy life expectancy, freedom to make life choices and the dependent variable happiness score, and there is negative correlation between perceptions of corruption and happiness score. However, there is no statistical significance for the coefficient of Generosity. Moreover, Model 2 and Model 3 also could not provide evidence to the effect of Generosity. Therefore, we could conclude that the improvement in economy, social support, healthy life expectancy, and freedom to make life choices could bring positive effect on people’s well-being. While the increase of perceptions of corruption could bring damage to people’s well-being. Generosity does not have obvious effect on people’s happiness index.
Model 2 shows the positive correlation between regime type and happiness score, which indicates that higher level democracy will increase the happiness scores. This provides support to the H1. However, the results in model 2 could not provide any evidence to the effect of the five demographic factors on happiness score.
In Model 3, the coefficient of population size and population density net change are negatively significant at 90% and 95% level of confidence, accordingly, showing that as the population size and population density net change increase, people’s well-being or their feeling of happiness will decrease, which partly supports H2. The results have two levels of meanings. Firstly, the countries with larger population size are less likely to provide better well-being for people than the countries with smaller population size, holding the other conditions all same. Secondly, as population density increases, people are less likely to feel happy when the other conditions are been held same. As for the regression of Model 4, there are only positive statistical significance for change in social support and change in freedom to make life choice, indicating that the increase of social support and the freedom in making life choices could improve country’s happiness index in 2020. In Model 5, change in social support is still positively statistically significant. Besides, change in GDP per capita, change in freedom to make life choices and change in generosity also impose positive effect on the change in happiness score, while there is no significance for change in healthy life expectancy, change in perception of corruption, migrants (net) and change in population density. Combined the results of Model 4 and Model 5, we could conclude that change in social support plays the most significant role in the improvement of people’s well-being in 2020. Besides, improving economy, providing more freedom for people, and increasing generosity are also good ways to go to improve people’s well-being.
Overall, we could conclude that GDP per capita (logged), social support, healthy life expectancy, freedom to make life choices, and regime type (democracy degree are positively correlated to one country’s average happiness index, while people’s perceptions of corruption is negatively correlated to the happiness index. Improvement in social support, freedom to make life choices, GDP per capita (logged), and generosity could bring positive effect on people’s well-being. Two of the demographic variables, including population size and population density net change are negatively correlated with people’s happiness. Finally, all COVID-19 severity measurement variables have no significant effect on either happiness index or the change in happiness index between 2019 and 2020. However, the add of demographic variables and COVID-19 severity measurement variables increase R2 and adjusted R2 to a large degree, indicating there might be some correlation between these variables between the two dependent variables. In the next section, I will use data visualization to explore further.
